4 repository-uri
Algorithms for maintaining object identity across multiple camera views.
Distinct from Camera Tracking: Distinct from Camera Tracking: focuses on re-identification across cameras rather than camera motion estimation.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · Cross-Camera Tracking. Refine with filters or upvote what's useful.
PaddleDetection is an object detection framework designed for the end-to-end development, training, and deployment of computer vision models. It provides a comprehensive library of modular neural network architectures and pipelines that support object detection, instance segmentation, and multi-object tracking tasks. The project distinguishes itself through a configuration-driven approach that decouples model components like backbones and heads, allowing for the flexible assembly of custom vision workflows. It incorporates advanced techniques such as anchor-free detection logic, joint detecti
Maintains object identity as targets move between different camera views.
jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti
Maintains unique object identities across a network of multiple cameras to handle occlusions.
Follows shoppers through a store by stitching together video feeds from multiple cameras to analyze movement patterns.
This project is a computer vision framework designed for the detection, identification, and tracking of human subjects within video streams. It provides an integrated system for locating individuals, generating biometric models from image datasets, and maintaining identity labels across consecutive video frames. The system distinguishes itself through its ability to maintain identity persistence across multiple camera feeds. By utilizing deep learning inference to extract feature vector embeddings and applying motion prediction algorithms, it links unique identity signatures across disparate
Maintains identity labels for individuals as they move between different camera perspectives.